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An experimental framework for Arabic digits speech recognition in noisy environments

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Abstract

In this paper we present an experimental framework for Arabic isolated digits speech recognition named ARADIGITS-2. This framework provides a performance evaluation of Modern Standard Arabic devoted to a Distributed Speech Recognition system, under noisy environments at various Signal-to-Noise Ratio (SNR) levels. The data preparation and the evaluation scripts are designed by deploying a similar methodology to that followed in AURORA-2 database. The original speech data contains a total of 2704 clean utterances, spoken by 112 (56 male and 56 female) Algerian native speakers, down-sampled at 8 kHz. The feature vectors, which consist of a set of Mel Frequency Cepstral Coefficients and log energy, are extracted from speech samples using ETSI Advanced Front-End (ETSI-AFE) standard; whereas, the Hidden Markov Models (HMMs) Toolkit is used for building the speech recognition engine. The recognition task is conducted in speaker-independent mode by considering both word and syllable as acoustic units. Therefore, an optimal fitting of HMM parameters, as well as the temporal derivatives window, is carried out through a series of experiments performed on the two training modes: clean and multi-condition. Better results are obtained by exploiting the polysyllabic nature of Arabic digits. These results show the effectiveness of syllable-like unit in building Arabic digits recognition system, which exceeds word-like unit by an overall Word Accuracy Rate of 0.44 and 0.58% for clean and multi-condition training modes, respectively.

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Acknowledgements

This work has been supported in part by the LCPTS laboratory project. We would like to thank Dr Abderrahmane Amrouche for making many suggestions which have been exceptionally helpful in carrying out this research work. We also would like to thank Dr. Amr Ibrahim El-Desoky Mousa for providing support in interpreting results.

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Authors and Affiliations

  1. Speech Communication and Signal Processing Laboratory (LCPTS), Faculty of Electronics and Computer Science, University of Science and Technology Houari Boumediene (USTHB), Bab Ezzouar, Algiers, Algeria

    Azzedine Touazi & Mohamed Debyeche

  2. Center for Development of Advanced Technologies (CDTA), Baba Hassen, Algiers, Algeria

    Azzedine Touazi

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  1. Azzedine Touazi
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Correspondence to Azzedine Touazi.

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Touazi, A., Debyeche, M. An experimental framework for Arabic digits speech recognition in noisy environments. Int J Speech Technol 20, 205–224 (2017). https://doi.org/10.1007/s10772-017-9400-x

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